CLASSIFICATION INTO MUSICAL GENRES USING A RHYTHMIC KERNEL

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Abstract:

Beginning with the question on how to determine the
genre of a music piece, we elaborate on the
representation of rhythm for the classification into
genres. The aim of such classification differs in principle
from that of traditional Music Informa tion Retrieval
algorithms. First, we formalise the rhythmic
representation of music fragments. This formalism is
then used to construct a similarity function called kernel.
To allow the discrete comparison of rhythmic fragments,
a pre-processing step in the algorithm computes a
common quantization unit among the input data. A
simple injective mapping into
N
allows the kernel to
employ the Euclidean dot product. A small database of
jazz, classical and rock fragments is used in an
implementation of a Support Vector Machine. The issues
that arise with different time signatures are analysed.
Finally, we share some early results of the experiments
comparing the three genres, showing that rhythm
conveys good information for classification, within the
conditions of the experiment.